An empty-nest power user identification method based on weighted random forest algorithm

Zimeng LU; Jiayi CHEN; Jing LI; Yue XIE; Xinli JIANG; Lei HAN; Qian GUO

doi:10.11959/j.issn.1000-0801.2020249

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An empty-nest power user identification method based on weighted random forest algorithm

Research and Development | 更新时间：2024-06-05

- An empty-nest power user identification method based on weighted random forest algorithm
- Telecommunications Science Vol. 36, Issue 8, Pages: 112-121(2020)
- 作者机构：
  
  1. 中国计量大学机电工程学院，浙江杭州 310018
  2. 国网金华供电公司，浙江金华 321000
  3. 浙江华云信息科技有限公司，浙江杭州 310018
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.2020249
  CLC： TP181，TP311，F426
- Published Online：2020-08，
  
  Published：20 August 2020
- 稿件说明：
移动端阅览
Zimeng LU, Jiayi CHEN, Jing LI, et al. An empty-nest power user identification method based on weighted random forest algorithm[J]. Telecommunications science, 2020, 36(8): 112-121.
DOI：

Zimeng LU, Jiayi CHEN, Jing LI, et al. An empty-nest power user identification method based on weighted random forest algorithm[J]. Telecommunications science, 2020, 36(8): 112-121. DOI： 10.11959/j.issn.1000-0801.2020249.

摘要

针对当前政府和社会对空巢老人的识别缺乏有效技术手段的问题，提出了一种基于加权随机森林算法的空巢电力用户识别方法。首先通过调查问卷获取部分准确空巢用户标签，并从用电水平、用电波动、用电趋势 3 个方面构建用户用电特征库，由于空巢与非空巢存在用户数据不平衡问题，采用加权随机森林算法改善机器学习对数据敏感的现象，将该算法模型在电力公司采集系统部署上线，并对2 000户未知类型用户进行空巢识别，其空巢识别准确率达到 74.2%。结果表明，从用电角度研究对空巢老人的识别，可以帮助电网公司了解空巢老人的个性化、差异化需求，从而为用户提供更精细的服务，也可以协助政府和社会开展帮扶工作。

Abstract

In view of the lack of effective technical means for the identification of empty-nesters by the government and the society

an empty-nesters prow user identification method based on weighted random forest algorithm was proposed.Firstly

some accurate labels of empty-nest users were obtained through questionnaires

and electricity characteristic library was drawn from three aspects:electricity consumption level

electricity consumption fluctuation and electricity consumption trend.Due to the data imbalance between empty-nest and non-empty-nest users

the weighted random forest algorithm was used to improve the data sensitivity phenomenon of machine learning.Finally

the algorithm model was put online in the power company’s acquisition system.The 2 000 unknown users of various types were identified

among which the identification accuracy of empty-nest users was 74.2%.The results show that the identification of empty-nesters from the perspective of electricity consumption can help power grid companies to understand the personalized and differentiated needs of empty-nesters

so as to provide users with more sophisticated services

and also assist the government and society to carry out assistance work.

关键词

Keywords

references

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